Trend analysis and forecasting of the Gökırmak River streamflow (Turkey)

Arslan, Gökhan; Kale, Semih; Sönmez, Adem Yavuz

doi:10.1515/ohs-2020-0021

Cited by 10 publications

(6 citation statements)

References 68 publications

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“…Data-driven approaches and physical observations are used to predict SST. Data-driven approaches range between traditional stochastic techniques, such as regression analysis (Kug et al 2004), empirical canonical correlation analysis (Collins et al 2004), trend analysis (Kale et al 2016a(Kale et al ,b, 2018Ejder et al 2016a,b;Kale & Sönmez 2018a,b;2019a,b,c;, Arslan et al 2020, Markov model (Xue & Leetmaa 2000), genetic algorithms (Neetu et al 2011), and modern artificial intelligence approaches, such as neural networks (Patil et al 2016), adaptive neuro-fuzzy inference systems (Sönmez et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)

Kale

2020

Oceanological and Hydrobiological Studies

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An accurate estimation of the sea surface temperature (SST) is of great importance. Therefore, the objective of this work was to develop an adaptive neuro-fuzzy inference system (ANFIS) model to predict SST in the Çanakkale Strait. The observed monthly air temperature, evaporation and precipitation data from the Çanakkale meteorological observation station were used as input data. The Takagi–Sugeno fuzzy inference system was applied. The grid partition method (ANFIS-GP) and the subtractive clustering partitioning method (ANFIS-SC) were used with Gaussian membership functions to generate the fuzzy inference system. Six performance evaluation criteria were used to evaluate the developed SST prediction models, including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE) and correlation of determination (R2). The dataset was randomly divided into training and testing datasets for the machine learning process. Training data accounted for 75% of the dataset, while 25% of the dataset was allocated for testing in ANFIS. The hybrid algorithm was selected as a training algorithm for the ANFIS. Simulation results revealed that the ANFIS-SC4 model provided a higher correlation coefficient of 0.96 between the observed and predicted SST values. The results of this study suggest that the developed ANFIS model can be applied for predicting sea surface temperature around the world.

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Section: Discussionmentioning

confidence: 99%

Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)

Kale

2020

Oceanological and Hydrobiological Studies

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“…Kişi (2015) indicated those restrictive assumptions as of the length of the data, normal distribution, and independent structure of the time series. Trend analysis was commonly applied to hydrometeorological time series by many scientists (Kale et al, 2016a(Kale et al, and 2016b(Kale et al, and 2018Ejder et al, 2016a, and2016b;Kale, 2017a, and2017b;Kale and Sönmez, 2018aand 2018band 2019a, and 2019band 2019cKale, 2020, Arslan et al, 2020). Likewise, Şen's innovative trend analysis methodology was also frequently applied to hydroclimatological time series observed at different locations through the world (Şen, 2014 and2015;Kişi et al, 2015;Ay et al, 2018;Gedefaw et al, 2018;Alifujiang et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Trend analysis commonly used in statistical analysis for economical, hydrometeorological, geophysical, environmental, and related time series. Many scientists applied trend analysis to hydrological or climatic time series (Kale et al, 2016a(Kale et al, and 2016b(Kale et al, and 2018Ejder et al, 2016a and2016b;Kale, 2017a and2017b;Kale and Sönmez, 2018aand 2018band 2019aand 2019band 2019cArslan et al, 2020). However, there is no investigation on the assessment of the trends in freshwater crayfish production in Turkey.…”

Section: Introductionmentioning

confidence: 99%

Trend Analysis and Comparison of Forecast Models for Production of Turkish Crayfish (Pontastacus leptodactylus Eschscholtz, 1823) in Turkey

Kale

Berber

2020

Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi

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This paper aimed to develop forecasting models and to assess the trends in the production of freshwater crayfish in Turkey. Different trend analysis methods (Box-Jenkins method, Şen's innovative trend analysis, Mann-Kendall test) were compared, and different autoregressive integrated moving average (ARIMA) models were generated to forecast the future trend. The results of the innovative trend analysis methodology and ARIMA models revealed that freshwater crayfish production has a decreasing trend during the study period, although there are some fluctuations. ARIMA models predicted that the production of freshwater crayfish would continue to decrease in the future period. The present paper is also the most temporally rich assessment of the crayfish production in Turkey encompassing 100+ years from 1909 to 2018. Several factors such as climate change, overexploitation, diseases, legal regulations, and fisheries management policies might affect the production amount. Therefore, appropriate policies for fisheries management and legal regulations should be planned and implemented to improve the production.

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“…This method has frequently been preferred because of its simple structure, it is used easily without any assumptions, and the trends and scatterings are identified through the charts. In the ITA methodology, the trend determination over different subgroups such as visually low, medium, and high rather than holistically and the development flexibility of methods and outputs attract the attention of many researchers (Sonali and Kumar 2013;Dabanlı et al 2016;Alashan 2018;Morbidelli et al2018;Ahmad et al 2018;Güçlü et al 2019;Şişman 2019;2021;Wang et al 2020;Malik et al 2020;Oruc 2020;Arslan et al 2020 and others).…”

Section: Introductionmentioning

confidence: 99%

The application of piecewise ITA method in Oxford, 1870–2019

Şişman

Kizilöz

2021

Theor Appl Climatol

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In this study, the trends and stabilities of temperature and precipitation hydro-meteorology time series recorded since 1870 in Oxford city of England were analyzed in detail. The Innovative Triangular Trend Analysis (ITTA) method has been inspired to identify and analyze the trends and stabilities of the selected time series. To compare the results obtained by the abovementioned method, the Classical Mann Kendall (MK) method has been applied to each series determined for ITTA design. Thanks to the innovative design of ITTA which is preferred by the Classic MK and Sen slope methods, the trends of time series could be analyzed in detail. In this study, the first draft structure has been improved with the help of ±5-±10 % percentage change levels which were added to the ITTA method, and thus more objective evaluations about the trend magnitudes in time series is possible. For the same draft, the monotonic trend slopes which were found by the classical MK were also calculated through the Sen slope method. The data trends could explain in more detail with the help of the draft used in this study, compared to the studies in the literature. Climate change, which has been the most important factor in trend formation in recent years, has been taken into consideration while determining the design series. The thirty-year period up to 2019, a year in which the climate change was felt much more, constitutes the most important reference years for the analysis beginning from 1990, a year in which the climate change effects started to emerge. When the data trends of one hundred fifty years are examined for the different sub-time series, it is seen that the temperature increase in during1990-2019 period is much higher than the past hundred and twenty years, according to the analysis results. The highest average precipitation occurred in the 1990-2019 and 1900-1929 periods, and their amounts and patterns are nearly similar.

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Trend analysis and forecasting of the Gökırmak River streamflow (Turkey)

Cited by 10 publications

References 68 publications

Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)

Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)

Trend Analysis and Comparison of Forecast Models for Production of Turkish Crayfish (Pontastacus leptodactylus Eschscholtz, 1823) in Turkey

The application of piecewise ITA method in Oxford, 1870–2019

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